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Neoplasm Staging

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The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest.

European radiology
OBJECTIVES: The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classi...

Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit.

Clinical cancer research : an official journal of the American Association for Cancer Research
Current tumor-node-metastasis (TNM) staging system cannot provide adequate information for prediction of prognosis and chemotherapeutic benefits. We constructed a classifier to predict prognosis and identify a subset of patients who can benefit from...

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artificial intelligence in medicine
Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially ...

Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma.

BMC bioinformatics
BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of ...

Comparison of Models for Predicting Quality of Life After Surgical Resection of Hepatocellular Carcinoma: a Prospective Study.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: The essential issue of internal validity has not been adequately addressed in prediction models such as artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and multiple linear regression (MLR)...

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BMC cancer
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...

High-Grade Serous Ovarian Cancer: Use of Machine Learning to Predict Abdominopelvic Recurrence on CT on the Basis of Serial Cancer Antigen 125 Levels.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance.

Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer.

Journal of gynecologic oncology
OBJECTIVE: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial in...

Stage-Specific Survivability Prediction Models across Different Cancer Types.

AMIA ... Annual Symposium proceedings. AMIA Symposium
For all cancer types, survivability rates vary widely across different stages of cancer. But survivability prediction models built in past were trained using examples of all stages together and were also evaluated on all stages together. In this work...